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Call for papers - Artificial intelligence and palliative care

Guest Editors

Matthew Allsop, PhD, CPsychol, University of Leeds, UK
Karl Arthur Lorenz, MD, MSHS, Stanford University School of Medicine, USA 
Christoph Ostgath, Dr med, University Hospital Erlangen, Germany 

Submission Status: Open   |   Submission Deadline: 23 April 2025

This Collection seeks to gather innovative research at the intersection of AI and palliative care, exploring the potential of AI-driven tools to optimize patient care, symptom management, and communication in the context of life-limiting illnesses. We invite submissions that showcase the latest advancements in AI applications for palliative care, with a focus on improving patient outcomes and enhancing the delivery of palliative care services.

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health & Well-Being.

Meet the Guest Editors

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Matthew Allsop, PhD, CPsychol, University of Leeds, UK

Dr Matthew Allsop is an Associate Professor of Palliative Care in the School of Medicine, University of Leeds, UK. His research focuses on the development and evaluation of palliative care service provision, including the design and evaluation of technology-based interventions. Dr Allsop’s research includes multiple nationally influential studies on the timing of hospice provision before death in the UK and determining the optimal role of digital technologies to support advance care planning in palliative care populations. Dr Allsop’s international health research has involved leading multiple initiatives to design and evaluate digital technology approaches to augment existing palliative care provision in low- and middle-income country settings. His work incorporates principles of equitable partnership working and embedding community engagement and involvement practices into the design, conduct and evaluation of research projects to ensure activities reflect and address local and context-specific challenges. 

Karl Arthur Lorenz, MD, MSHS, Stanford University School of Medicine, USA

Dr Karl Lorenz is Professor of Medicine at Stanford University in Palo Alto, CA. Previously, he was Professor at UCLA and adjunct faculty at the RAND Corporation in Los Angeles, CA. Throughout his career, he served as a palliative care and general internal medicine physician with the United States Department of Veterans Affairs (VA). As a member of VA’s leadership team, he contributes to the growth of palliative care across VA’s 150+ hospitals and health systems as the Co-Director of the national Quality Improvement Resource Center (QuIRC). His research has encompassed quality measurement and improvement, implementation science, evidence synthesis, symptom management, and global health. 

Christoph Ostgath, Dr med, University Hospital Erlangen, Germany 

Prof Dr Christoph Ostgathe was trained as an anesthesiologist, pain and palliative care specialist. In 2010 he was appointed as Professor for Palliative Medicine at the University of Erlangen-Nuernberg. His clinic comprises an acute palliative care unit, a palliative care hospital support team and an outpatient service. His research is mainly dedicated to issues of Screening for Palliative Care Needs, Outcome Assessment and Health Services Research, currently focusing e.g. on Intentional Sedation. His second focus is the integration of innovative medical technology and digitalization, including AI, into palliative care. From 2019–2023 he was President of the European Association for Palliative Care (EAPC). 

About the Collection

Artificial intelligence (AI) and machine learning have been playing an increasing role in healthcare, offering opportunities to enhance symptom management, prognostication, and personalized care for patients with life-limiting illnesses. In palliative care, AI applications can encompass a wide range of areas, including but not limited to decision support systems, predictive modeling, natural language processing (NLP) for patient communication, education, and the analysis of large datasets to identify patterns and trends in patient care. By using AI in novel ways, it has the potential to improve patient outcomes and the delivery of palliative care services.

Recent advances have demonstrated the feasibility of using AI algorithms to predict patient deterioration, optimize pain management strategies, and tailor care plans to individual patient needs. Additionally, AI-driven tools have shown promise in facilitating more efficient resource allocation and enhancing communication between healthcare providers, patients, and their families.

This Collection invites submissions exploring the multifaceted landscape of AI applications in palliative care, with a particular focus on elucidating these applications and addressing the ethical dimensions in their implementation. Potential topics of interest include: 

  • AI-driven decision support systems in palliative care
  • Machine learning for prognostication in end-of-life care
  • Natural language processing for patient communication in palliative care
  • Ethical considerations in the use of AI in palliative care
  • AI applications for symptom management in palliative care
  • AI as a tool to foster palliative care training and education  
  • AI applications for audiovisual monitoring to enhance patient comfort and functional assessment
  • Further uses in palliative care

This Collection supports and amplifies research related to SDG 3 (Good Health and Well-Being).


Image credit: © [M] Parradee / stock.adobe.com

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Artificial intelligence and palliative care" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.